Bacteria in blood identification using electronic nose data based on LSTM and BILSTM deep neural network models

Abstract

Bacteria are single-celled organisms that en- ter the body, grow, and release toxins that harm cells, causing sepsis and other diseases. Because bacteria cause various diseases in humans, prompt diagnosis is required to adapt antibiotic medication and prevent disease spread. This study presents a promising de- vice that can distinguish between different types of bac- teria commonly found in the blood. Electronic nose technology is now regarded as a quick tool for detect- ing pathologies based on volatile organic compounds (VOCs). The use of classical bacteriology takes time to give the practitioner or biologist a diagnosis. The bacterial species is detected from VOCs released by bac- teria in a few minutes using a multi-sensor system for the detection of VOCs. The goal of this study was to test and identify ten different types of bacteria in blood by an electronic nose. The proposed models achieved accuracies of 96.77% (LSTM) and 98.91% (Bi-LSTM), demonstrating the superiority of Bi-LSTM for bacterial classification.